Point cloud geometry compression

ABSTRACT

A system comprises an encoder configured to compress a point cloud comprising a plurality of points each point comprising spatial information for the point. The encoder is configured to sub-sample the points and determine subdivision locations for the subsampled points. Also, the encoder is configured to determine, for respective subdivision location, if a point is to be included, not included, or relocated relative to the subdivision location. The encoder encodes spatial information for the sub-sampled points and encodes subdivision location point inclusion/relocation information to generate a compressed point cloud. A decoder recreates an original or near replica of an original point cloud based on the spatial information and the subdivision location inclusion/relocation information included in the compressed point cloud.

PRIORITY DATA

This application is a continuation of U.S. patent application Ser. No.16/569,433, filed Sep. 12, 2019, which is a continuation of U.S. patentapplication Ser. No. 16/121,501, filed Sep. 4, 2018, which claimsbenefit of priority to U.S. Provisional Application Ser. No. 62/555,003,filed Sep. 6, 2017, which are hereby incorporated by reference in theirentirety.

BACKGROUND Technical Field

This disclosure relates generally to compression and decompression ofpoint clouds comprising a plurality of points each having associatedspatial information and, in some embodiments, additional attributeinformation.

Description of the Related Art

Various types of sensors, such as light detection and ranging (LIDAR)systems, 3-D-cameras, 3-D scanners, etc. may capture data indicatingpositions of points in three dimensional space, for example positions inthe X, Y, and Z planes. Also, some such systems may further captureattribute information in addition to spatial information for therespective points, such as color information (e.g. RGB values),intensity attributes, reflectivity attributes, or various otherattributes. In some circumstances, other attributes may be assigned tothe respective points, such as a time-stamp when the point was captured.Points captured by such sensors may make up a “point cloud” comprising aset of points each having associated spatial information. In somecircumstances, a point cloud may include thousands of points, hundredsof thousands of points, millions of points, or even more points. Also,in some circumstances, point clouds may be generated, for example insoftware, as opposed to being captured by one or more sensors. In eithercase, such point clouds may include large amounts of data and may becostly and time-consuming to store and transmit.

SUMMARY OF EMBODIMENTS

In some embodiments, a system includes one or more sensors configured tocapture points that collectively make up a point cloud, wherein each ofthe points comprises spatial information identifying a spatial locationof the respective point. The system also includes an encoder configuredto generate a compressed point cloud, wherein the compressed point cloudcomprises spatial information for fewer points than the number of pointsof the captured point cloud, but wherein the spatial information forpoints included in the compressed point cloud and additional dataincluded in the compressed point cloud is organized in such a way that adecoder may recreate the captured point cloud or a close approximationof the captured point cloud based on the compressed point cloud. Togenerate the compressed point cloud, the encoder is configured tosub-sample the captured point cloud captured by the one or more sensors,wherein the sub-sampled point cloud comprises fewer points than thecaptured point cloud. The encoder is further configured to, for each ofrespective ones of the points of the sub-sampled point cloud, identify alocation between the respective point of the sub-sampled point cloud anda neighboring point in the sub-sampled point cloud and determine, basedon comparing the location to the captured point cloud, whether a pointin a decompressed point cloud is to be included at the location, notincluded at the location, or relocated relative to the location. Theencoder is further configured to encode data for the compressed pointcloud comprising spatial information for the points of the sub-sampledpoint cloud and data indicating, for each of the respective locations,whether a respective point is to be included at the location, notincluded at the location, or relocated relative to the location in thedecompressed point cloud. In some embodiments, a system may omit the oneor more sensors and the encoder may receive an original point cloud, tobe compressed, from sensors of another system, or from another source.

In some embodiments, a method includes sub-sampling a point cloud,wherein the sub-sampled point cloud comprises fewer points than thepoint cloud. The method also includes, for each of respective ones ofthe points of the sub-sampled point cloud, comparing a location betweenthe respective point of the sub-sampled point cloud and a neighboringpoint in the sub-sampled point cloud to the point cloud prior to thesub-sampling and determining, based on the comparison, whether: a pointis to be included at the location, not included at the location, orrelocated relative to the location in a decompressed point cloud. Themethod further includes encoding data comprising spatial information forthe points of the sub-sampled point cloud and data indicating, for eachof the respective locations, whether a respective point is to beincluded at the location, not included at the location, or relocatedrelative to the location in a decompressed point cloud.

In some embodiments, a non-transitory computer-readable medium storesprogram instructions that, when executed by one or more processors,cause the one or more processors to implement a decoder configured toreceive a compressed point cloud, wherein the compressed point cloudcomprises spatial information for points of a sub-sampled point cloudand data indicating, for each of a plurality of respective locationsbetween respective points of the sub-sampled point cloud and respectiveneighboring points in the sub-sampled point cloud, whether a point is tobe included at the respective location, not included at the respectivelocation, or relocated relative to the respective location in adecompressed point cloud. The program instructions, when executed,further cause the decoder to, for each of respective ones of the pointsof the sub-sampled point cloud, identify a respective location betweenthe respective point and a neighboring point in the sub-sampled pointcloud and determine, based on the data included in the receivedcompressed point cloud, whether to include, not include, or relocate apoint at the respective location. The program instructions, whenexecuted, further cause the decoder to generate the decompressed pointcloud, wherein the decompressed point cloud comprises the points of thesub-sampled point cloud and the respective points determined to beincluded at the respective locations or relocated relative to therespective locations.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A illustrates a system comprising a sensor that capturesinformation for points of a point cloud and an encoder that compresses apoint cloud, wherein the compressed point cloud is sent to a decoder,according to some embodiments.

FIG. 1B illustrates a process for encoding a compressed point cloud,according to some embodiments.

FIG. 1C illustrates representative views of a point cloud at differentstages of an encoding process, according to some embodiments.

FIG. 2A illustrates components of an encoder, according to someembodiments.

FIG. 2B illustrates components of a decoder, according to someembodiments.

FIG. 3 illustrates components of an example compressed point cloud file,according to some embodiments.

FIG. 4 illustrates a process for encoding a compressed point cloud,according to some embodiments.

FIG. 5 illustrates a process for determining subdivision locations for asub-sampled point cloud when encoding a compressed point cloud,according to some embodiments.

FIGS. 6A-C illustrate a process for decoding a compressed point cloud,according to some embodiments.

FIG. 7 illustrates a compressed point cloud being used in a 3-Dtelepresence application, according to some embodiments.

FIG. 8 illustrates a compressed point cloud being used in a virtualreality application, according to some embodiments.

FIG. 9 illustrates an example computer system that may implement anencoder or decoder, according to some embodiments.

This specification includes references to “one embodiment” or “anembodiment.” The appearances of the phrases “in one embodiment” or “inan embodiment” do not necessarily refer to the same embodiment.Particular features, structures, or characteristics may be combined inany suitable manner consistent with this disclosure.

“Comprising.” This term is open-ended. As used in the appended claims,this term does not foreclose additional structure or steps. Consider aclaim that recites: “An apparatus comprising one or more processor units. . . ” Such a claim does not foreclose the apparatus from includingadditional components (e.g., a network interface unit, graphicscircuitry, etc.).

“Configured To.” Various units, circuits, or other components may bedescribed or claimed as “configured to” perform a task or tasks. In suchcontexts, “configured to” is used to connote structure by indicatingthat the units/circuits/components include structure (e.g., circuitry)that performs those task or tasks during operation. As such, theunit/circuit/component can be said to be configured to perform the taskeven when the specified unit/circuit/component is not currentlyoperational (e.g., is not on). The units/circuits/components used withthe “configured to” language include hardware—for example, circuits,memory storing program instructions executable to implement theoperation, etc. Reciting that a unit/circuit/component is “configuredto” perform one or more tasks is expressly intended not to invoke 35U.S.C. § 112(f), for that unit/circuit/component. Additionally,“configured to” can include generic structure (e.g., generic circuitry)that is manipulated by software and/or firmware (e.g., an FPGA or ageneral-purpose processor executing software) to operate in manner thatis capable of performing the task(s) at issue. “Configure to” may alsoinclude adapting a manufacturing process (e.g., a semiconductorfabrication facility) to fabricate devices (e.g., integrated circuits)that are adapted to implement or perform one or more tasks.

“First,” “Second,” etc. As used herein, these terms are used as labelsfor nouns that they precede, and do not imply any type of ordering(e.g., spatial, temporal, logical, etc.). For example, a buffer circuitmay be described herein as performing write operations for “first” and“second” values. The terms “first” and “second” do not necessarily implythat the first value must be written before the second value.

“Based On.” As used herein, this term is used to describe one or morefactors that affect a determination. This term does not forecloseadditional factors that may affect a determination. That is, adetermination may be solely based on those factors or based, at least inpart, on those factors. Consider the phrase “determine A based on B.”While in this case, B is a factor that affects the determination of A,such a phrase does not foreclose the determination of A from also beingbased on C. In other instances, A may be determined based solely on B.

DETAILED DESCRIPTION

As data acquisition and display technologies have become more advanced,the ability to capture point clouds comprising thousands or millions ofpoints in 2-D or 3-D space, such as via LIDAR systems, has increased.Also, the development of advanced display technologies, such as virtualreality or augmented reality systems, has increased potential uses forpoint clouds. However, point cloud files are often very large and may becostly and time-consuming to store and transmit. For example,communication of point clouds over private or public networks, such asthe Internet, may require considerable amounts of time and/or networkresources, such that some uses of point cloud data, such as real-timeuses, may be limited. Also, storage requirements of point cloud filesmay consume a significant amount of storage capacity of devices storingthe point cloud files, which may also limit potential applications forusing point cloud data.

In some embodiments, an encoder may be used to generate a compressedpoint cloud to reduce costs and time associated with storing andtransmitting large point cloud files. In some embodiments, a system mayinclude an encoder that compresses a point cloud file such that thepoint cloud file may be stored and transmitted more quickly thannon-compressed point clouds and in a manner that the point cloud filemay occupy less storage space than non-compressed point clouds. In someembodiments, point cloud compression may enable a point cloud to becommunicated over a network in real-time or in near real-time. Forexample, a system may include a sensor that captures data about pointsin an environment where the sensor is located, wherein the capturedpoints make up a point cloud. The system may also include an encoderthat compresses the captured point cloud. The compressed point cloud maybe sent over a network in real-time or near real time to a decoder thatdecompresses the compressed point cloud. The decompressed point cloudmay be further processed, for example to make a control decision basedon the surrounding environment at the location of the sensor. Thecontrol decision may then be communicated back to a device at or nearthe location of the sensor, wherein the device receiving the controldecision implements the control decision in real time or near real time.In some embodiments, the decoder may be associated with an augmentedreality system and the decompressed point cloud may be displayed orotherwise used by the augmented reality system.

In some embodiments, a system may include a decoder that receives one ormore point cloud files via a network from a remote server or otherstorage device that stores the one or more point cloud files. Forexample, a 3-D display, a holographic display, or a head-mounted displaymay be manipulated in real-time or near real-time to show differentportions of a virtual world represented by point clouds. In order toupdate the 3-D display, a holographic display, or the head-mounteddisplay, a system associated with the decoder may request point cloudfiles from the remote server based on user manipulations of thedisplays, and the point cloud files may be transmitted from the remoteserver to the decoder and decoded by the decoder in real time or nearreal-time. The displays may then be updated with updated point clouddata responsive to the user manipulations.

In some embodiments, a system, may include one or more LIDAR systems,3-D cameras, 3-D scanners, etc., and such sensor devices may capturespatial information, such as X, Y, and Z coordinates for points in aview of the sensor devices. In some embodiments, the spatial informationmay be relative to a local coordinate system or may be relative to aglobal coordinate system (for example, a Cartesian coordinate system mayhave a fixed reference point, such as a fixed point on the earth, or mayhave a non-fixed local reference point, such as a sensor location). Insome embodiments, such sensors may also capture attribute informationfor one or more of the points, such as color attributes, reflectivityattributes, velocity attributes, acceleration attributes, timeattributes, and/or various other attributes.

In some embodiments, an encoder may receive a point cloud that is to becompressed and may sub-sample the point cloud to generate a sub-sampledpoint cloud. For example, in some embodiments, a point cloud maycomprise a million points, and a sub-sampled point cloud generated bysub-sampling the point cloud may include as few as 1,000 or 2,000points. In some embodiments, sub-sampling may be performed at uniformpoint intervals or uniform distances. For example, in some embodiments,sub-sampling may comprise including every 100^(th), 1,000^(th), orN^(th) point in the point cloud in the sub-sampled point cloud, or maycomprise including a point at every D increment of distance in the X, Y,or Z direction in the sub-sampled point cloud. In some embodiments,sub-sampling may include filtering points in a point cloud, for exampleto reduce aliasing. In some embodiments, other sub-sampling techniquesmay be used.

Once the sub-sampled point cloud is generated, the encoder may identifya set of one or more neighboring points for each of a plurality ofrespective points of the sub-sampled point cloud. For example, theencoder may select a first point in the sub-sampled point cloud and mayidentify a set of nearest neighboring points in the sub-sampled pointcloud to the selected point being evaluated. For each of the identifiedneighboring points, the encoder may determine a subdivision locationbetween the selected point being evaluated and the respectiveneighboring points of the set of nearest neighboring points. The encodermay also compare the subdivision location between the point beingevaluated and the respective neighboring point to the original pointcloud prior to the sub-sampling. If the location is within a range of apoint included in the original point cloud, the encoder may determinethat a point is to be included in a decompressed point cloud at thesubdivision location and may include an indication in data encoded for acompressed point cloud indicating inclusion of a point at thesubdivision location. If the location is outside of a range from pointsincluded in the original point cloud, the encoder may determine that apoint is not to be included at the subdivision location in adecompressed point cloud, and may include such an indication in data fora compressed point cloud. Also, the encoder may determine that a pointis within the range to be included in the decompressed point cloud butis at a distance away from a point included in the original point cloud.In such a circumstance, the encoder may include information in data forthe compressed point cloud indicating a point is to be relocatedrelative to the subdivision location in a decompressed point cloud. Forexample, the encoder may compare a location of a point in the originalpoint cloud adjacent to the subdivision location to the location of thesubdivision location to determine how a point is to be relocatedrelative to the subdivision location. The encoder may continue thisprocess for each identified neighboring point in the set of neighboringpoints identified for the selected point being evaluated. The encodermay then repeat a similar process for a next point in the sub-sampledpoint cloud that is to be evaluated until at least a significant portionof the points in the sub-sampled point cloud have been evaluated.

Additionally, the encoder may compare the sub-sampled point cloud andpoints determined to be included at the respective locations betweenpoints of the sub-sampled point cloud (e.g. subdivisions) or points tobe relocated relative to the respective locations at the subdivisionsand may determine that one or more additional points are to be includedin the decompressed point cloud to accurately represent the originalpoint cloud. In some embodiments, spatial information for the one ormore additional points may be explicitly encoded in data for thecompressed point cloud.

In some embodiments, an encoder may perform multiple iterations ofdetermining points to be located or relocated at subdivision locationsof a sub-sampled point cloud and may perform multiple iterations ofdetermining one or more additional points that are to be included in adecompressed point cloud. For example, an encoder may update thesub-sampled point cloud to include the points at the subdivisionlocations that are determined to be included in the decompressed pointcloud, to include the points determined to be relocated relative to thesubdivision locations, and to include the additional points determinedto be included in the decompressed point cloud. The encoder may thenrepeat the subdivision process by selecting respective ones of thepoints included in the updated sub-sampled point cloud, identifyingnearest neighboring points of the selected points, and determiningwhether to include, not include, or relocate points at subdivisionlocations between the selected points and the nearest neighboringpoints. The encoder may also determine based on comparing the updatedsub-sampled point cloud including any points determined to be includedat newly subdivided locations whether or not one or more additionalpoints are to be included in the decompressed point cloud. The encodermay repeat this process for multiple iterations. In some embodiments, anencoder and decoder may perform iterations per a pre-specified ordetermined number of subdivision iterations to be performed (N). In someembodiments, a number of subdivision iteration to be performed (N) maybe a user-configurable parameter or may be a parameter determined at theencoder based on the point cloud data being processed, or a combinationthereof. For example in some embodiments the number of iterations to beperformed may be based on an average, minimum, or maximum value betweenthe user configurable parameter and an encoder based derived parameter.In some embodiments, a number of subdivision iterations to be performedmay be a fixed value. In some embodiments, a number of subdivisioniterations to be performed may be pre-determined and known by both theencoder and decoder. In some embodiments, an encoder may include dataindicating a number of subdivision iterations to be performed in datafor a compressed point cloud.

The encoder may encode the spatial information for the points of thesub-sampled point cloud and information indicating whether points are tobe included at subdivision locations, not included at subdivisionlocations, or relocated relative to subdivision locations. Additionally,spatial information for any additional points determined to be includedin the decompressed point cloud may be encoded.

An encoder may also encode configuration information, such as describedabove, to be sent along with sub-sampled point data and subdivisionpoint inclusion, non-inclusion, or relocation data for a compressedpoint cloud. For example, an encoder may encode data indicating a numberof subdivision iterations (N) that are to be performed, a number ofnearest neighbors that are to be included in a set of nearest neighbors(K), a minimum distance from a point for a nearest neighbor whenidentifying nearest neighbors (D0), a maximum distance from a point fora nearest neighbor when identifying nearest neighbors (D1), a thresholddistance for a location of a subdivision when determining whether apoint is to be included at a subdivision location (D2), etc. In someembodiments, the distances D0, D1, and/or D2 may be computed asEuclidian distances between a point being evaluated and a nearestneighboring point or a subdivision location. The configurationinformation may enable a decoder to recreate the same subdivisionlocations as were evaluated at the encoder based on the spatialinformation of the points of the sub-sampled point cloud. In this way,the decoder may recreate the original point cloud or a closerepresentation of the original point cloud while only receiving spatialinformation for a portion of the points of the original point cloud(e.g. the spatial information of the sub-sampled points of the pointcloud). Because explicit spatial information for far fewer points isincluded in the compressed point cloud data, the compressed point clouddata may occupy less storage space than non-compressed data for theoriginal or captured point cloud and may be transmitted more quicklyand/or with fewer network resources than non-compressed data for theoriginal or captured point cloud.

FIG. 1A illustrates a system comprising a sensor that capturesinformation for points of a point cloud and an encoder that compresses apoint cloud that is sent to a decoder, according to some embodiments.

System 100 includes sensor 102 and encoder 104. Sensor 102 captures apoint cloud 110 comprising points representing structure 106 in view 108of sensor 102. For example, in some embodiments, structure 106 may be amountain range, a building, a sign, an environment surrounding a street,or any other type of structure. In some embodiments, a captured pointcloud, such as captured point cloud 110, may include spatial and/orattribute information for the points included in the point cloud. Forexample, point A of captured point cloud 110 comprises X, Y, Zcoordinates and attributes 1, 2, and 3. In some embodiments, attributesof a point may include attributes such as R, G, B color values, avelocity of the structure at the point, an acceleration of the structureat the point, a reflectance of the structure at the point, a time stampindicating when the point was captured, or other attributes. Thecaptured point cloud 110 may be provided to encoder 104, wherein encoder104 generates a compressed version of the point cloud (compressed pointcloud 112) that is transmitted via network 114 to decoder 116.

In some embodiments, encoder 104 may be integrated with sensor 102. Forexample, encoder 104 may be implemented in hardware or software includedin a sensor device, such as sensor 102. In other embodiments, encoder104 may be implemented on a separate computing device that is proximateto sensor 102.

FIG. 1B illustrates a process for encoding a compressed point cloud,according to some embodiments. Also, FIG. 1C illustrates representativeviews of a point cloud at different stages of an encoding process,according to some embodiments.

At 152, an encoder, such as encoder 104, receives a captured pointcloud, such as captured point cloud 110 illustrated in FIG. 1A orcaptured point cloud 154 illustrated in FIG. 1C. Note that capturedpoint cloud 110 and 154 are illustrated in two dimensions for ease ofillustration, but in some embodiments may comprise three-dimensionalpoint clouds.

At 156, the encoder sub-samples the point cloud to generate asub-sampled point cloud. For example, FIG. 1C illustrates sub-sampledpoint cloud 158. In some embodiments, an encoder may uniformly samplethe point cloud to generate the sub-sampled point cloud. For example,the encoder may select points at uniform intervals in a point cloud togenerate a sub-sampled point cloud. In some embodiments, an encoder mayuse a lossy compression algorithm when encoding spatial information of asub-sampled point cloud to be used to generate a decompressedsub-sampled point cloud. In some such embodiments, an encoder may encodethe sub-sampled point cloud using the lossy compression algorithm andmay decode, at the encoder, the lossy encoded sub-sampled point cloud togenerate a representative sub-sampled point cloud that a decoder willencounter when decoding spatial information for points of a sub-sampledpoint cloud encoded according to the lossy compression algorithm. Insome embodiments, an encoder may use a lossless compression algorithmfor encoding spatial information for points of a sub-sampled pointcloud, and the encoder may omit encoding and decoding the sub-sampledpoint cloud to generate a representative sub-sampled point cloud to beencountered by a decoder. This is because when using a losslesscompression algorithm, the sub-sampled point cloud is representative ofwhat the decoder will encounter when decoding the spatial informationfor the points of the sub-sampled point cloud that were encodedaccording to the lossless compression algorithm.

At 160, the encoder identifies subdivision locations between respectivepoints of the sub-sampled point cloud and neighboring points of therespective points of the sub-sampled point cloud. For example asillustrated in 162 of FIG. 1C. At 164, the encoder determines whether apoint is to be included in the decompressed point cloud at thesubdivision location, not included in the decompressed point cloud atthe subdivision location, or relocated in the decompressed point cloudrelative to the subdivision location. For example, as illustrated in 166of FIG. 1C.

In some embodiments, multiple subdivision locations may be determinedbetween a given point and respective ones of a set of neighboring pointsto the given point and a determination may be made for each one of themultiple subdivision locations as to whether a point is to be includedin the decompressed point cloud at the subdivision location, notincluded in the decompressed point cloud at the subdivision location, orrelocated in the decompressed point cloud relative to the subdivisionlocation. In each instance, the determination to include, not include,or relocate a point relative to a subdivision location may be determinedbased on comparing the subdivision location to the original point cloudprior to the sub-sampling. For example, if the subdivision locationfalls on a surface of the original point cloud, the encoder maydetermine a point should be included at the subdivision location in thedecompressed point cloud. Conversely, if the subdivision location ismore than a threshold distance from a surface of the original pointcloud, the encoder may determine that a point is not to be included atthe subdivision location. If the subdivision location is within athreshold range of a surface of the original point cloud, but would needto be adjusted to align with the surface of the original point cloud,the encoder may determine to relocate a point in the decompressed pointcloud relative to the subdivision location such that the relocated pointmore closely aligns with the original point cloud.

In some embodiments, an encoder may further encode temporal informationin addition to spatial information. For example, each point may have atime attribute associated with the point, such as a time when the pointwas captured. In some embodiments, in which temporal information isbeing encoded, the encoder may further determine whether to include, notinclude, or relocate a point relative to a subdivision location based ontemporal information, such as whether or not the point cloud has changedat the subdivision location from one time increment to the next, as anexample. Or, based on whether a point was included at the subdivisionlocation in a previous version of the decompressed point cloud at anearlier moment in time.

In some embodiments, an encoder may determine that one or moreadditional points are to be included in a decompressed point cloud. Forexample, if the original captured point cloud has an irregular surfaceor shape such that subdivision locations between points in thesub-sampled point cloud do not adequately represent the irregularsurface or shape, the encoder may determine to include one or moreadditional points in addition to points determined to be included atsubdivision locations or relocated relative to subdivision locations inthe decompressed point cloud. In some embodiments, data representing theadditional points may be explicitly encoded in addition to thesub-sampled points included in the data for the compressed point cloud.

In some embodiments an encoder may perform 160 and 164 for each point ina sub-sampled point cloud. Also, in some embodiments, an encoder may addany points determined to be included or relocated relative to a locationand any other additional points determined to be included in adecompressed point cloud to generate an updated sub-sampled point cloud.The encoder may then perform 160 and 164 for each of the points of theupdated sub-sampled point cloud. In some embodiments, an encoder maycontinue to iterate through 160 and 164 and update a sub-sampled pointcloud for a particular number of iterations, for example a specified ordetermined parameter (N). In some embodiments, a number of subdivisioniterations to be performed may be a configurable parameter that isconfigured at the encoder and communicated to a decoder along with thecompressed point cloud. In some embodiments, an encoder and decoder maybe configured to perform a fixed number of subdivision iterations.

Once the encoder has completed evaluating the respective subdivisionlocations to determine if a point is to be included, not included, orrelocated at each of the respective subdivision locations and hasiterated through this process for all the subdivision iterationsrequired per the iteration parameter, the encoder may, at 168, encodethe spatial information and/or attribute information for the points ofthe sub-sampled point cloud and may also encode data indicating for eachof the respective subdivision locations whether a point is to beincluded, or not included at the respective subdivision location. Forpoints that are to be relocated in the decompressed point cloud, theencoder may further encode location correction data indicating how thepoint is to be relocated relative to the subdivision location. In someembodiments, the encoder may additionally encode spatial and/orattribute information for additional points to be added to thedecompressed point cloud and may encode other configuration information.

For example, 166 of FIG. 1C illustrates points at subdivision locationsbeing relocated to align with the original captured point cloud. In someembodiments, this location correction information may be encoded asdelta X, delta Y, or delta Z spatial information relative to thesubdivision location. In other embodiments, this location correctioninformation may be encoded as a scalar value that may be determined viaa dot product between a vector from the subdivision location to therelocated location and a normal vector at the subdivision location,where the normal vector is normal to a surface of the sub-sampled pointcloud prior to relocating the point to the relocated location. In someembodiments, location correction data may be arithmetically encoded orencoded according to Golomb encoding techniques, or other suitablelossless encoding techniques.

In some embodiments, an encoder may further encode temporal informationin addition to spatial information. For example, each point may have atime associated with the point, such as a time when the point wascaptured. In some embodiments, in which temporal information is beingencoded, the encoder may further determine whether to include, notinclude, or relocate a point relative to a subdivision location based ontemporal information, such as whether or not the point cloud has changedat the subdivision location from one time increment to the next, as anexample. As another example, an encoder may determine that a pointincluded at a particular time increment in an original (non-compressed)point cloud that does not appear at previous time increments (e.g.frames) in the original point cloud and/or that does not appear atsubsequent time increments (e.g. frames) in the original point cloud isnot an important point. In response to such a determination, an encodermay determine that a point does not need to be included at a subdivisionlocation corresponding to the point in the original point cloud. Anencoder may utilize similar temporal considerations when determiningwhether or not to relocate a point relative to a sub-division locationand/or when determining whether to include spatial information foradditional points in a point cloud.

Additionally, spatial information for any additional points determinedto be included in a decompressed point cloud may be encoded and includedin the compressed point cloud. Also, data indicating configurableparameters, such as a number of nearest neighbors (K) to include in aset of nearest neighbors for a point when determining subdivisionlocations, a minimum distance from a selected point (D0) for nearestneighbors, a maximum distance from a selected point (D1) for nearestneighbors, a minimum distance from a selected point for a subdivisionlocation (D2), a distance between a selected point and a nearestneighbor for a subdivision location (M), for example a midpoint, anumber of subdivision iterations to perform (N), etc. may be included ina compressed point cloud sent from an encoder to a decoder or includedin a compressed point cloud that is to be stored and later decoded by adecoder. In some embodiments, this additional information included inthe compressed point cloud may be encoded using various encodingtechniques. For example, arithmetic encoding, Golomb encoding or othersuitable lossless or lossy encoding techniques may be used.

Because the compressed point cloud includes spatial information for thesub-sampled points, data indicating inclusion or non-inclusion of pointsat subdivision locations, data indicating relocation of any points to berelocated relative to subdivision locations, spatial information for anyadditional points to be included, and configuration information used bythe encoder, the decoder can recreate the original captured point cloudor a near replica of the original captured point cloud by performingsimilar analysis as the encoder. For example, the decoder may determinesubdivision locations using the same configuration parameters as theencoder and may include, not include, or relocate points at thesubdivision locations per the determinations included in the compressedpoint cloud. Also, the decoder may include additional points based onthe spatial information for the additional points included in thecompressed point cloud. A decoder may also determine whether to includeadditional points based on temporal parameters, such as changes in pointlocations from frame to frame in a multi-frame point cloud.

FIG. 2A illustrates components of an encoder, according to someembodiments.

Encoder 202 may be a similar encoder as encoder 104 illustrated in FIG.1A. Encoder 202 includes sub-sampler 216, lossy encoder 204 and lossydecoder 210. As described above, in some embodiments a lossy encodingalgorithm may be used to encode spatial information of points of asub-sampled point cloud. Thus, an encoder, such as encoder 202, mayreceive a point cloud, for example via incoming data interface 214 andmay sub-sample the received point cloud, via a sub-sampler, such assub-sampler 216, to generate a sub-sampled point cloud. The encoder, maythen lossy encode and lossy decode the sub-sampled point cloud, forexample via lossy encoder 204 and lossy decoder 210, to generate arepresentative sub-sampled point cloud that represents a sub-sampledpoint cloud a decoder will generate when receiving a compressed pointcloud comprising sub-sampled points that have been lossy encoded.

In some embodiments, an encoder, such as encoder 202, may furtherinclude a configuration interface, such as configuration interface 212,wherein one or more parameters used by the encoder to compress a pointcloud may be adjusted via the configuration interface. In someembodiments, a configuration interface, such as configuration interface212, may be a programmatic interface, such as an API. Configurationsused by an encoder, such as encoder 202, may be stored in aconfiguration store, such as configuration store 218. A subdivisionevaluator, such as subdivision location evaluator 206, may performsubdivision location evaluations to determine if points should beincluded, not included, or relocated relative to subdivision locations.Also, a subdivision location evaluator may determine any additionalpoints that are to be included in a decompressed point cloud generatedfrom data included in a compressed point cloud encoded by the encoder.

Once subdivision location decision information has been determined andany additional points that are to be included in the decompressed pointcloud have been determined, the spatial information for the points ofthe sub-sampled point cloud, the data indicating subdivision locationdecision information, relocation information, spatial information forany additional points that are to be included in a decompressed pointcloud, and configuration information for use in decoding a compressedpoint cloud may be encoded via an outgoing data encoder, such asoutgoing data encoder 208. In some embodiments, an outgoing data encodermay encode outgoing data according to any of various encoding techniquessuch as arithmetic encoding, Golomb encoding, various lossless encodingtechniques, or lossy encoding techniques. In some embodiments, spatialinformation for points may be encoded according to a lossy encodingtechnique while relocation information for points that are to berelocated relative to a subdivision location may be encoded according toa lossless encoding technique. In some embodiments, relocationinformation comprising a few characters may be encoded using aparticular encoding technique while relocation information comprisingmore characters may be encoded using a different encoding technique.

In some embodiments, an encoder, such as encoder 202, may include moreor fewer components than shown in FIG. 2A. For example, in someembodiments that encode spatial information using a lossless encodingtechnique, lossy encoder 204 and lossy decoder 210 may be omitted.

FIG. 2B illustrates components of a decoder, according to someembodiments.

Decoder 220 may be a similar decoder as decoder 116 illustrated in FIG.1A. Decoder 220 includes encoded data interface 224, sub-sample decoder222, subdivision point evaluator 226, configuration store 230, anddecoded data interface 228.

A decoder, such as decoder 220, may receive an encoded compressed pointcloud from an encoder, such as compressed point cloud 112 illustrated inFIG. 1A or compressed point cloud file 300 illustrated in FIG. 3, via anencoded data interface, such as encoded data interface 224. The encodedcompressed point cloud may be decoded and used by the decoder todetermine points of a sub-sampled point cloud. For example, spatialinformation of points of a sub-sampled point cloud may be decoded by asub-sample decoder, such as sub-sample decoder 222. In some embodiments,a compressed point cloud may be received via an encoded data interface,such as encoded data interface 224, from a storage device or otherintermediary source, wherein the compressed point cloud was previouslyencoded by an encoder, such as encoder 104.

Once a sub-sampled point cloud is determined, a subdivision pointevaluator, such as subdivision point evaluator 226, may determineneighboring points for respective points of the determined sub-sampledpoint cloud and may determine subdivision locations between therespective points and the respective neighboring points. The subdivisionevaluator may also receive decode data included in a compressed pointcloud indicating whether a point is to be included or not included at aparticular subdivision location, or if the point is to be relocatedrelative to the subdivision location. In some embodiments, thesub-sample decoder 222 may also decode, via lossy or losslesstechniques, encoded data indicating whether a point is to be included ornot included at a particular subdivision location and/or relocationinformation and provide the decoded data to the subdivision pointevaluator 226. The subdivision evaluator may determine the subdivisionlocations based on parameters stored in a configuration store, such asconfiguration store 230. In some embodiments, at least some of theconfigurations may be configurable parameters that are configured at anencoder and communicated to the decoder along with the compressed pointcloud. In some embodiments, at least some of the parameters may be fixedparameters that are not communicated with a compressed point cloud. Insome embodiments, configurable parameters included with a compressedpoint cloud may be decoded and added to a configuration store, such asconfiguration store 230, such that the configurable parameters are usedby the subdivision point evaluator 226 when determining subdivisionlocations. In some embodiments, a subdivision point evaluator and/or asub-sample decoder may further decode spatial information for anyadditional points that are to be included in the decompressed pointcloud, wherein the spatial information for the additional points isincluded in the encoded compressed point cloud received by the decoder.

A decoder, such as decoder 220, may provide a decompressed point cloudgenerated based on a received encoded compressed point cloud to areceiving device or application via a decoded data interface, such asdecoded data interface 228. The decompressed point cloud may include thepoints of the sub-sampled point cloud, any points to be included at orrelocated relative to a subdivision locations, and any additional pointsindicated to be included in the decompressed point cloud. In someembodiments, the decompressed point cloud may be used to generate avisual display, such as for a head mounted display. Also, in someembodiments, the decompressed point cloud may be provided to a decisionmaking engine that uses the decompressed point cloud to make one or morecontrol decisions. In some embodiments, the decompressed point cloud maybe used in various other applications or for various other purposes.

FIG. 3 illustrates an example compressed point cloud file, according tosome embodiments. Point cloud file 300 includes configurationinformation 304, point cloud data 306, subdivision location pointinclusion/relocation data 308, and additional points to be included in adecompressed point cloud 310. In some embodiments, point cloud file 300may be communicated in parts via multiple packets. In some embodiments,not all of the sections shown in point cloud file 300 may be included ineach packet transmitting compressed point cloud information. In someembodiments, a point cloud file, such as point cloud file 300 may bestored in a storage device, such as a server that implements an encoderor decoder, or may be stored in another computing device.

FIG. 4 illustrates a process for encoding a compressed point cloud,according to some embodiments.

At 402, an encoder receives a point cloud. The point cloud may be acaptured point cloud from one or more sensors or may be a generatedpoint cloud, such as a point cloud generated by a graphics application.

At 404, the encoder sub-samples the received point cloud to generate asub-sampled point cloud. The sub-sampled point cloud may include fewerpoints than the received point cloud. For example, the received pointcloud may include hundreds of thousands of points or millions of pointsand the sub-sampled point cloud may include hundreds of points orthousands of points.

At 406 the encoder encodes and decodes the sub-sampled point cloud togenerate a representative sub-sampled point cloud the decoder willgenerate when decoding the compressed point cloud. In some embodiments,the encoder and decoder may execute a lossy compression/decompressionalgorithm to generate the representative sub-sampled point cloud. Insome embodiments, spatial information for points of a sub-sampled pointcloud may be quantized as part of generating a representativesub-sampled point cloud. In some embodiments, an encoder may utilizelossless compression techniques and 406 may be omitted. For example,when using lossless compression techniques the original sub-sampledpoint cloud may be representative of a sub-sampled point cloud thedecoder will generate because in lossless compression data may not belost during compression and decompression.

At 408, the encoder identifies subdivision locations between points ofthe sub-sampled point cloud according to configuration parametersselected for compression of the point cloud or according to fixedconfiguration parameters. The configuration parameters used by theencoder that are not fixed configuration parameters are communicated toa decoder by including values for the configuration parameters in acompressed point cloud. Thus, a decoder may determine the samesubdivision locations as the encoder evaluated based on subdivisionconfiguration parameters included in the compressed point cloud. FIG. 5discusses how an encoder identifies subdivision locations according toconfiguration parameters in more detail.

At 410, the encoder determines for respective ones of the subdivisionlocations whether a point is to be included or not included at thesubdivision location in a decompressed point cloud. Data indicating thisdetermination is encoded in the compressed point cloud. In someembodiments, the data indicating this determination may be a single bitthat if “true” means a point is to be included and if “false” means apoint is not to be included. Additionally, an encoder may determine thata point that is to be included in a decompressed point cloud is to berelocated relative to the subdivision location in the decompressed pointcloud. For such points, the encoder may further encode data indicatinghow to relocate the point relative to the subdivision location. In someembodiments, location correction information may be quantized andentropy encoded. In some embodiments, the location correctioninformation may comprise delta X, delta Y, and/or delta Z valuesindicating how the point is to be relocated relative to the subdivisionlocation. In other embodiments, the location correction information maycomprise a single scalar value which corresponds to the normal componentof the location correction information computed as follows:ΔN=([X _(A) ,Y _(A) ,Z _(A)]−[X,Y,Z])·[Normal Vector]

In the above equation, delta N is a scalar value indicating locationcorrection information that is the difference between the relocated oradjusted point location relative to the subdivision location (e.g.[X_(A), Y_(A), Z_(A)]) and the original subdivision location (e.g. [X,Y, Z]). The cross product of this vector difference and the normalvector at the subdivision location results in the scalar value delta N.Because a decoder can determine, the normal vector at the subdivisionlocation, and can determine the coordinates of the subdivision location,e.g. [X, Y, Z], the decoder can also determine the coordinates of theadjusted location, e.g. [X_(A), Y_(A), Z_(A)], by solving the aboveequation for the adjusted location, which represents a relocatedlocation for a point relative to the subdivision location. In someembodiments, the location correction information may be furtherdecomposed into a normal component and one or more additional tangentialcomponents. In such an embodiment, the normal component, e.g. delta N,and the tangential component(s) may be quantized and encoded forinclusion in a compressed point cloud.

At 412, the encoder determines whether one or more additional points (inaddition the sub-sampled points and in addition to points included atsubdivision locations or points included at locations relocated relativeto subdivision locations) are to be included in a decompressed pointcloud. For example, if the original point cloud has an irregular surfaceor shape such that subdivision locations between points in thesub-sampled point cloud do not adequately represent the irregularsurface or shape, the encoder may determine to include one or moreadditional points in addition to points determined to be included atsubdivision locations or relocated relative to subdivision locations inthe decompressed point cloud. Additionally, an encoder may determinewhether one or more additional points are to be included in adecompressed point cloud based on system constraints, such as a targetbitrate, a target compression ratio, a quality target metric, etc. Insome embodiments, a bit budget may change due to changing conditionssuch as network conditions, processor load, etc. In such embodiments, anencoder may adjust a quantity of additional points that are to beincluded in a decompressed point cloud based on a changing bit budget.In some embodiments, an encoder may include additional points such thata bit budget is consumed without being exceeded. For example, when a bitbudget is higher, an encoder may include more additional points toconsume the bit budget (and enhance quality) and when the bit budget isless, the encoder may include fewer additional points such that the bitbudget is consumed but not exceeded.

At 414, the encoder determines whether additional subdivision iterationsare to be performed. If so, the points determined to be included,relocated, or additionally included in a decompressed point cloud aretaken into account and the process reverts to 408 to identify newsubdivision locations of an updated sub-sampled point cloud thatincludes the points determined to be included, relocated, oradditionally included in a decompressed point cloud. In someembodiments, a number of subdivision iterations to be performed (N) maybe a fixed or configurable parameter of an encoder/decoder. In someembodiments, different subdivision iteration values may be assigned todifferent portions of a point cloud. For example, an encoder may takeinto account a point of view from which the point cloud is being viewedand may perform more subdivision iterations on points of the point cloudin the foreground of the point cloud as viewed from the point of viewand fewer subdivision iterations on points in a background of the pointcloud as viewed from the point of view.

At 416, the spatial information for the sub-sampled points of the pointcloud are encoded and included in the compressed point cloud.Additionally, subdivision location inclusion and relocation data isencoded and included in the compressed point cloud along with spatialinformation for any additional points that are to be included in adecompressed point cloud. Additionally, any configurable parametersselected by the encoder or provided to the encoder from a user areencoded to be included with the compressed point cloud. The compressedpoint cloud may then be sent to a receiving entity as a compressed pointcloud file, multiple compressed point cloud files, or may be packetizedand communicated via multiple packets to a receiving entity, such as adecoder or a storage device.

FIG. 5 illustrates a process for sub-dividing a sub-sampled point cloud,according to some embodiments. Steps 502-512 illustrated in FIG. 5 maybe performed as part of identifying subdivision locations, such as in408 in FIG. 4.

At 502, a subdivision location evaluator, such as subdivision locationevaluator 206 of encoder 202 illustrated in FIG. 2A, selects a selectedpoint from a sub-sampled point cloud, such as a sub-sampled point cloudresulting from subsampling at 404 in FIG. 4 or a representativesub-sampled point cloud, such as resulting from 406 in FIG. 4. In someembodiments, points of a sub-sampled point cloud may be ordered, and thesubdivision location evaluator may select the first (or next) point inthe order. The sub-sampled points may be communicated to the decoder inthe same order, such that the decoder selects points to be evaluatedfrom the sub-sampled point cloud or the representative sub-sampled pointcloud in the same order as they were selected for evaluation by theencoder.

At 504, the subdivision location evaluator identifies the nearestneighboring point to the selected point (or in subsequent iterations,the next nearest neighboring point). At 506, the subdivision locationevaluator determines if the identified nearest or next nearestneighboring point is at least a minimum distance (D0) from the selectedpoint being evaluated. In some embodiments, D0 may be a configurableparameter that the encoder communicates to a decoder in a compressedpoint cloud. If the nearest or next nearest neighboring point is closerto the selected point being evaluated than the minimum distance D0, theneighboring point is not further evaluated for a subdivision location,and the process reverts to 504, wherein a next nearest neighboring pointis selected.

If the identified neighboring point being evaluated at 506 is fartheraway from the selected point than the minimum distance, it is determinedat 508 whether the identified neighboring point being evaluated iscloser to the selected point being evaluated than a maximum distance(D1). D1 also may be a configurable parameter that the encodercommunicates to a decoder in a compressed point cloud. If the identifiedneighboring point being evaluated is farther away from the selectedpoint being evaluated than the maximum distance (D1), the identifiedneighboring point being evaluated may not be further evaluated for asubdivision location and the process may revert to 504 wherein anotherneighboring point is selected, or if no neighboring points are within D0and D1, the process may complete and revert to 410 of FIG. 4 (e.g. 512of FIG. 5).

At 510, a subdivision location, between the selected point beingevaluated and the identified neighboring point being evaluated, isevaluated to determine if the subdivision location is at least at aminimum distance away from the selected point being evaluated (D2). Insome embodiments the subdivision location may be at a midpoint betweenthe selected point being evaluated and the identified neighboring pointbeing evaluated. In some embodiments, the subdivision location may be ata location other than a midpoint between the selected point beingevaluated and the identified neighboring point being evaluated. Forexample, in some embodiments, an encoder may select a configurationparameter for determining a subdivision location between a selectedpoint being evaluated and an identified neighboring point beingevaluated. This parameter (M) may be communicated to a decoder in acompressed point cloud.

If the subdivision location is greater than the minimum distance D2 fromthe selected point being evaluated, the subdivision may be a validsubdivision location and the encoder may determine if a point is to beincluded at the subdivision location, not included at the subdivisionlocation, or relocated relative to the subdivision location at 512. Notethat 512 may be the same as 410 in FIG. 4, and the encoder may continuewith steps 412-416 shown in FIG. 4.

FIG. 6 illustrates a process for decoding a compressed point cloud,according to some embodiments. A decoder may perform similar steps asdescribed for an encoder in FIGS. 4 and 5 to generate a decompressedpoint cloud based on information included in a compressed point cloudreceived from an encoder following a similar process as outlined inFIGS. 4 and 5.

At 602, the decoder receives a compressed point cloud. The compressedpoint cloud may include spatial information for points of a sub-sampledpoint cloud, information indicating inclusion or relocation of points atsubdivision locations, spatial information for any additional points tobe included in a decompressed point cloud, and configurationinformation. In some embodiments, the sub-sampled point cloud may becommunicated to the decoder in an encoded format. For example thespatial information for the points of the sub-sampled point cloud mayhave been encoded using an arithmetic encoding technique or a Golombencoding technique at an encoder. In such embodiments, the decoder maydecode the encoded spatial information for the points of the sub-sampledpoint cloud, such that the spatial information for the points of thesub-sampled point cloud can be used to determine additional points to beincluded in a decompressed point cloud generated from the compressedpoint cloud.

At 604, a subdivision point evaluator of a decoder, such as subdivisionpoint evaluator 226 of decoder 220 illustrated in FIG. 2B, selects apoint from the sub-sampled point cloud communicated in the compressedpoint cloud to evaluate for subdivision locations. In subsequentiterations, the selected point may be a point of an updated sub-sampledpoint cloud that includes points determined to be included in adecompressed point cloud in a previous iteration of determiningsubdivision locations. In some embodiments, the points of thesub-sampled point cloud may be ordered such that the subdivision pointevaluator of the decoder selects points of the sub-sampled point cloudto be evaluated in a same order as which the points were evaluated at anencoder.

At 606, the subdivision point evaluator of the decoder identifies anearest (or next nearest) neighboring point to the selected point of thesub-sampled point cloud being evaluated. At 608, the subdivision pointevaluator of the decoder determines if the selected neighboring point isat least at a minimum distance (D0) from the selected point beingevaluated. If the neighboring point is not at least at the minimumdistance D0 from the selected point being evaluated, the subdivisionpoint evaluator reverts to 606 and selects another neighboring point. Ifthe neighboring point is at a greater distance than the minimum distanceD0 from the selected point being evaluated, at 610 the subdivision pointevaluator determines if the neighboring points is within a maximumdistance D1 from the selected point being evaluated. If the neighboringpoint is not within the maximum distance D1 from the selected pointbeing evaluated, the subdivision point evaluator reverts to 606 andselects another neighboring point. If there are not any neighboringpoints within D0 and D1, the subdivision point evaluator reverts to 604and evaluates another point.

If the neighboring point is within D0 and D1, at 612 the subdivisionpoint evaluator determines a subdivision location between the selectedpoint being evaluated and the current neighboring point being evaluated.In some embodiments, the subdivision location may be a midpoint, or maybe at a location other than a midpoint, wherein configurationinformation included in the compressed point cloud indicates how asubdivision location is to be determined between a point and aneighboring point. At 614, the subdivision point evaluator of thedecoder determines if the determined subdivision location is at least aminimum distance D2 from the selected point being evaluated. In someembodiments, the parameters D0, D1, and/or D2 may be communicated in acompressed point cloud or may be fixed parameters that are the same atboth the encoder and the decoder. If the determined subdivision locationis not at least at the minimum distance D2 from the selected point beingevaluated, the subdivision point evaluator of the decoder reverts to 606and identifies another neighboring point to evaluate.

At 616, once a valid subdivision location is determined, the subdivisionpoint evaluator determines, based on the subdivision location pointinclusion/relocation data included in the compressed point cloud,whether a point is to be included at the subdivision location. If apoint is not to be included at the subdivision location, the subdivisionpoint evaluator reverts to 606 and identifies another neighboring pointto evaluate.

At 618, the subdivision point evaluator determines if the point is to berelocated relative to the subdivision location. If the point is not tobe relocated, the subdivision point evaluator causes, at 620, a point tobe included at the subdivision location in the decompressed point cloud.If the point is to be relocated, the decoder, at 622, utilizesrelocation information included in the compressed point cloud todetermine a location relative to the subdivision location where a pointis to be included in the decompressed point cloud and causes, at 624,the point to be included in the decompressed point cloud.

At 626, the decoder determines based on configuration informationwhether there are additional neighboring points that are to be evaluatedfor the selected point. For example, a configuration parameter “K”included in the compressed point cloud may indicate a number ofneighboring points that are to be included in a set of neighboringpoints to be evaluated for subdivision locations for a given selectedpoint being evaluated. If there are additional neighboring points to beevaluated, the decoder may revert to 606, and the subdivision pointevaluator may evaluate another neighboring point that neighbors theselected point being evaluated. If there are not any additionalneighboring points to be evaluated for the selected point, the decodermay determine, at 628, if there are additional points of the sub-sampledpoint cloud that are to be evaluated. If there are additional points ofthe sub-sampled point cloud to be evaluated, the decoder may revert to604 and select the next point of the points of the sub-sampled pointcloud to evaluate and may perform the same process described above forthe next point in the sub-sampled point cloud. In some embodiments, thepoints of the sub-sampled point cloud may be communicated to the decoderin an order such that the decoder selects the points of the sub-sampledpoint cloud to be evaluated in a same order in which they were evaluatedat the encoder.

If there are not any additional points of the sub-sampled point cloud tobe evaluated, the decoder may determine at 630, if there are anyadditional points that are to be included in the decompressed pointcloud for the current subdivision iteration. If there are additionalpoints to be included, they may be included at 632. If not, 632 may beskipped.

At 634, the decoder determines if there are additional subdivisioniterations to be performed. For example, the compressed point cloud mayinclude a configuration parameter “N” indicating a number of subdivisioniterations that are to be performed when generating a decompressed pointcloud from the compressed point cloud. If there is an additionalsubdivision iteration to be performed, the decoder, at 636, updates thecurrent sub-sampled point cloud with any points determined to beincluded at a subdivision location or relocated relative to asubdivision location, along with any additional points determined to beincluded in the decompressed point cloud. Subsequent to updating thecurrent sub-sampled point cloud, the decoder reverts to 604 and repeatsthe process using the updated sub-sampled point cloud to determinesubdivision locations.

If there are not any additional subdivision iterations yet to beperformed, the decoder generates the decompressed point cloud at 638.The decompressed point cloud includes the points of the originalsub-sampled point cloud included in the compressed point cloud and anypoints determined to be included or relocated relative to anysubdivision locations determined during any of the subdivisioniterations, along with any additional points determined to be includedin the decompressed point cloud during any of the subdivisioniterations.

FIG. 7 illustrates compressed point clouds being used in a 3-Dtelepresence application, according to some embodiments.

In some embodiments, a sensor, such as sensor 102, an encoder, such asencoder 104 or encoder 202, and a decoder, such as decoder 116 ordecoder 220, may be used to communicate point clouds in a 3-Dtelepresence application. For example, a sensor, such as sensor 102, at702 may capture a 3D image and at 704, the sensor or a processorassociated with the sensor may perform a 3D reconstruction based onsensed data to generate a point cloud.

At 706, an encoder such as encoder 104 or 202 may compress the pointcloud and at 708 the encoder or a post processor may packetize andtransmit the compressed point cloud, via a network 710. At 712, thepackets may be received at a destination location that includes adecoder, such as decoder 116 or decoder 220. The decoder may decompressthe point cloud at 714 and the decompressed point cloud may be renderedat 716. In some embodiments a 3-D telepresence application may transmitpoint cloud data in real time such that a display at 716 representsimages being observed at 702. For example, a camera in a canyon mayallow a remote user to experience walking through a virtual canyon at716.

FIG. 8 illustrates compressed point clouds being used in a virtualreality (VR) or augmented reality (AR) application, according to someembodiments.

In some embodiments, point clouds may be generated in software (forexample as opposed to being captured by a sensor). For example, at 802virtual reality or augmented reality content is produced. The virtualreality or augmented reality content may include point cloud data andnon-point cloud data. For example, a non-point cloud character maytraverse a landscape represented by point clouds, as one example. At804, the point cloud data may be compressed and at 806 the compressedpoint cloud data and non-point cloud data may be packetized andtransmitted via a network 808. For example, the virtual reality oraugmented reality content produced at 802 may be produced at a remoteserver and communicated to a VR or AR content consumer via network 808.At 810, the packets may be received and synchronized at the VR or ARconsumer's device. A decoder operating at the VR or AR consumer's devicemay decompress the compressed point cloud at 812 and the point cloud andnon-point cloud data may be rendered in real time, for example in a headmounted display of the VR or AR consumer's device. In some embodiments,point cloud data may be generated, compressed, decompressed, andrendered responsive to the VR or AR consumer manipulating the headmounted display to look in different directions.

In some embodiments, point cloud compression as described herein may beused in various other applications, such as geographic informationsystems, sports replay broadcasting, museum displays, autonomousnavigation, etc.

Example Computer System

FIG. 9 illustrates an example computer system 900 that may implement anencoder or decoder or any other ones of the components described herein,(e.g., any of the components described above with reference to FIGS.1-8), in accordance with some embodiments. The computer system 900 maybe configured to execute any or all of the embodiments described above.In different embodiments, computer system 900 may be any of varioustypes of devices, including, but not limited to, a personal computersystem, desktop computer, laptop, notebook, tablet, slate, pad, ornetbook computer, mainframe computer system, handheld computer,workstation, network computer, a camera, a set top box, a mobile device,a consumer device, video game console, handheld video game device,application server, storage device, a television, a video recordingdevice, a peripheral device such as a switch, modem, router, or ingeneral any type of computing or electronic device.

Various embodiments of a point cloud encoder or decoder, as describedherein may be executed in one or more computer systems 900, which mayinteract with various other devices. Note that any component, action, orfunctionality described above with respect to FIGS. 1-8 may beimplemented on one or more computers configured as computer system 900of FIG. 9, according to various embodiments. In the illustratedembodiment, computer system 900 includes one or more processors 910coupled to a system memory 920 via an input/output (I/O) interface 930.Computer system 900 further includes a network interface 940 coupled toI/O interface 930, and one or more input/output devices 950, such ascursor control device 960, keyboard 970, and display(s) 980. In somecases, it is contemplated that embodiments may be implemented using asingle instance of computer system 900, while in other embodimentsmultiple such systems, or multiple nodes making up computer system 900,may be configured to host different portions or instances ofembodiments. For example, in one embodiment some elements may beimplemented via one or more nodes of computer system 900 that aredistinct from those nodes implementing other elements.

In various embodiments, computer system 900 may be a uniprocessor systemincluding one processor 910, or a multiprocessor system includingseveral processors 910 (e.g., two, four, eight, or another suitablenumber). Processors 910 may be any suitable processor capable ofexecuting instructions. For example, in various embodiments processors910 may be general-purpose or embedded processors implementing any of avariety of instruction set architectures (ISAs), such as the x86,PowerPC, SPARC, or MIPS ISAs, or any other suitable ISA. Inmultiprocessor systems, each of processors 910 may commonly, but notnecessarily, implement the same ISA.

System memory 920 may be configured to store point cloud compression orpoint cloud decompression program instructions 922 and/or sensor dataaccessible by processor 910. In various embodiments, system memory 920may be implemented using any suitable memory technology, such as staticrandom access memory (SRAM), synchronous dynamic RAM (SDRAM),nonvolatile/Flash-type memory, or any other type of memory. In theillustrated embodiment, program instructions 922 may be configured toimplement an image sensor control application incorporating any of thefunctionality described above. In some embodiments, program instructionsand/or data may be received, sent or stored upon different types ofcomputer-accessible media or on similar media separate from systemmemory 920 or computer system 900. While computer system 900 isdescribed as implementing the functionality of functional blocks ofprevious Figures, any of the functionality described herein may beimplemented via such a computer system.

In one embodiment, I/O interface 930 may be configured to coordinate I/Otraffic between processor 910, system memory 920, and any peripheraldevices in the device, including network interface 940 or otherperipheral interfaces, such as input/output devices 950. In someembodiments, I/O interface 930 may perform any necessary protocol,timing or other data transformations to convert data signals from onecomponent (e.g., system memory 920) into a format suitable for use byanother component (e.g., processor 910). In some embodiments, I/Ointerface 930 may include support for devices attached through varioustypes of peripheral buses, such as a variant of the Peripheral ComponentInterconnect (PCI) bus standard or the Universal Serial Bus (USB)standard, for example. In some embodiments, the function of I/Ointerface 930 may be split into two or more separate components, such asa north bridge and a south bridge, for example. Also, in someembodiments some or all of the functionality of I/O interface 930, suchas an interface to system memory 920, may be incorporated directly intoprocessor 910.

Network interface 940 may be configured to allow data to be exchangedbetween computer system 900 and other devices attached to a network 985(e.g., carrier or agent devices) or between nodes of computer system900. Network 985 may in various embodiments include one or more networksincluding but not limited to Local Area Networks (LANs) (e.g., anEthernet or corporate network), Wide Area Networks (WANs) (e.g., theInternet), wireless data networks, some other electronic data network,or some combination thereof. In various embodiments, network interface940 may support communication via wired or wireless general datanetworks, such as any suitable type of Ethernet network, for example;via telecommunications/telephony networks such as analog voice networksor digital fiber communications networks; via storage area networks suchas Fibre Channel SANs, or via any other suitable type of network and/orprotocol.

Input/output devices 950 may, in some embodiments, include one or moredisplay terminals, keyboards, keypads, touchpads, scanning devices,voice or optical recognition devices, or any other devices suitable forentering or accessing data by one or more computer systems 900. Multipleinput/output devices 950 may be present in computer system 900 or may bedistributed on various nodes of computer system 900. In someembodiments, similar input/output devices may be separate from computersystem 900 and may interact with one or more nodes of computer system900 through a wired or wireless connection, such as over networkinterface 940.

As shown in FIG. 9, memory 920 may include program instructions 922,which may be processor-executable to implement any element or actiondescribed above. In one embodiment, the program instructions mayimplement the methods described above. In other embodiments, differentelements and data may be included. Note that data may include any dataor information described above.

Those skilled in the art will appreciate that computer system 900 ismerely illustrative and is not intended to limit the scope ofembodiments. In particular, the computer system and devices may includeany combination of hardware or software that can perform the indicatedfunctions, including computers, network devices, Internet appliances,PDAs, wireless phones, pagers, etc. Computer system 900 may also beconnected to other devices that are not illustrated, or instead mayoperate as a stand-alone system. In addition, the functionality providedby the illustrated components may in some embodiments be combined infewer components or distributed in additional components. Similarly, insome embodiments, the functionality of some of the illustratedcomponents may not be provided and/or other additional functionality maybe available.

Those skilled in the art will also appreciate that, while various itemsare illustrated as being stored in memory or on storage while beingused, these items or portions of them may be transferred between memoryand other storage devices for purposes of memory management and dataintegrity. Alternatively, in other embodiments some or all of thesoftware components may execute in memory on another device andcommunicate with the illustrated computer system via inter-computercommunication. Some or all of the system components or data structuresmay also be stored (e.g., as instructions or structured data) on acomputer-accessible medium or a portable article to be read by anappropriate drive, various examples of which are described above. Insome embodiments, instructions stored on a computer-accessible mediumseparate from computer system 900 may be transmitted to computer system900 via transmission media or signals such as electrical,electromagnetic, or digital signals, conveyed via a communication mediumsuch as a network and/or a wireless link. Various embodiments mayfurther include receiving, sending or storing instructions and/or dataimplemented in accordance with the foregoing description upon acomputer-accessible medium. Generally speaking, a computer-accessiblemedium may include a non-transitory, computer-readable storage medium ormemory medium such as magnetic or optical media, e.g., disk orDVD/CD-ROM, volatile or non-volatile media such as RAM (e.g. SDRAM, DDR,RDRAM, SRAM, etc.), ROM, etc. In some embodiments, a computer-accessiblemedium may include transmission media or signals such as electrical,electromagnetic, or digital signals, conveyed via a communication mediumsuch as network and/or a wireless link.

The methods described herein may be implemented in software, hardware,or a combination thereof, in different embodiments. In addition, theorder of the blocks of the methods may be changed, and various elementsmay be added, reordered, combined, omitted, modified, etc. Variousmodifications and changes may be made as would be obvious to a personskilled in the art having the benefit of this disclosure. The variousembodiments described herein are meant to be illustrative and notlimiting. Many variations, modifications, additions, and improvementsare possible. Accordingly, plural instances may be provided forcomponents described herein as a single instance. Boundaries betweenvarious components, operations and data stores are somewhat arbitrary,and particular operations are illustrated in the context of specificillustrative configurations. Other allocations of functionality areenvisioned and may fall within the scope of claims that follow. Finally,structures and functionality presented as discrete components in theexample configurations may be implemented as a combined structure orcomponent. These and other variations, modifications, additions, andimprovements may fall within the scope of embodiments as defined in theclaims that follow.

What is claimed is:
 1. A non-transitory computer-readable medium storingprogram instructions that, when executed by one or more processors,cause the one or more processors to: for respective ones of a pluralityof points of a reduced-point version of a point cloud that comprisesspatial information for fewer points than a number of points included ina non-reduced-point version of the point cloud: predict a predictedpoint location between the respective point of the reduced-point versionof the point cloud and a neighboring point in the reduced-point versionof the point cloud; determine, based on comparing the predicted pointlocation to the non-reduced-point version of the point cloud, updateinformation for the predicted point location; and provide data for acompressed version of the point cloud, the data comprising thedetermined update information for the predicted point locations.
 2. Thenon-transitory computer-readable medium of claim 1, wherein the updateinformation for a respective one of the predicted point locationsindicates: whether a respective point is to be included at the predictedpoint location; and whether the respective point is to be relocatedrelative to the predicted point location in a decompressed version ofthe point cloud.
 3. The non-transitory computer-readable medium of claim1, wherein the program instructions, when executed by the one or moreprocessors, cause the one or more processors to: sub-sample thenon-reduced-point version of the point cloud to generate the reducedpoint version of the point cloud.
 4. The non-transitorycomputer-readable medium of claim 3, wherein the program instructions,when executed by the one or more processors, cause the one or moreprocessors to further: quantize the spatial information of the points ofthe reduced-point version of the point cloud.
 5. The non-transitorycomputer-readable medium of claim 4, wherein to provide the data for thecompressed version of the point cloud, the program instructions, whenexecuted by the one or more processors, cause the one or more processorsto: encode the quantized spatial information of the points of thereduced-point version of the point cloud using an arithmetic encodingtechnique or a Golomb encoding technique; and encode the data comprisingthe determined update information for the predicted point locationsusing the arithmetic encoding technique or the Golomb encodingtechnique.
 6. A non-transitory computer-readable medium storing programinstructions that, when executed by one or more processors, cause theone or more processors to: receive update information for predictedpoint locations between respective points of a reduced-point version ofa point cloud; for each of respective ones of the points of thereduced-point version of the point cloud: predict a respective predictedpoint location between the respective point and a neighboring point inthe reduced-point version of the point cloud; and apply the updateinformation for the predicted point location; and generate adecompressed version of the point cloud, wherein the decompressedversion of the point cloud comprises one or more points at the predictedpoint locations to which the update information has been applied.
 7. Thenon-transitory computer-readable medium of claim 6, wherein the programinstructions, when executed by the one or more processors further causethe one or more processors to: obtain the reduced-point version of thepoint cloud, wherein the reduced-point version is a spatially compressedversion of the point cloud than comprises fewer points than an originalversion of the point cloud prior to being compressed.
 8. Thenon-transitory computer-readable medium of claim 6, wherein to apply theupdate information for the predicted point locations, the programinstructions cause the one or more processors to: for each of respectiveones of the predicted point locations: determine whether to include, notinclude, or relocate a point at the respective predicted point locationin the decompressed version of the point cloud.
 9. The non-transitorycomputer-readable medium of claim 6, wherein to apply the updateinformation for a given one of the predicted point locations, theprogram instructions cause the one or more processors to: determine alocation for a point relocated relative to the given one of thepredicted point locations based on adding or subtracting residuallocation information to one or more coordinates of the given one of thepredicted point locations.
 10. The non-transitory computer-readablemedium of claim 6, wherein to predict the respective predicted pointlocations, the program instructions cause the one or more processors,for each of the respective ones of the points of the reduced-pointversion of the point cloud, to: identify a set of neighboring points,wherein the set comprises a number of nearest neighbors to be used forprediction according to a value indicated in the received data for thecompressed point cloud; and identify a location between the respectivepoint and each of the neighboring points of the set of neighboringpoints.
 11. A device, comprising: a memory storing program instructionsconfigured to implement a point cloud spatial compression algorithm; andone or more processors, wherein the program instructions, when executedon the one or more processors, cause the one or more processors to: forrespective ones of a plurality of points of a reduced-point version of apoint cloud that comprises spatial information for fewer points than anumber of points included in an non-reduced-point version of the pointcloud: predict a predicted point location between the respective pointof the reduced-point version of the point cloud and a neighboring pointin the reduced-point version of the point cloud; determine, based oncomparing the predicted point location to the non-reduced-point versionof the point cloud, update information for the predicted point location;and provide data for a compressed version of the point cloud, the datacomprising the determined update information for the predicted pointlocations.
 12. The device of claim 11, wherein to provide the data forthe compressed version of the point cloud, the program instructionsfurther cause the one or more processors to: encode the data comprisingthe determined update information for the predicted point locationsusing an arithmetic encoding technique or a Golomb encoding technique.13. The device of claim 11, wherein the program instructions, furthercause the one or more processors to: sub-sample the non-reduced-pointversion of the point cloud to generate the reduced point version of thepoint cloud.
 14. The device of claim 13, wherein the programinstructions, further cause the one or more processors to: encode thespatial information of the points of the reduced-point version of thepoint cloud using an arithmetic encoding technique or a Golomb encodingtechnique.
 15. The device of claim 11, wherein the update informationfor a respective one of the predicted point locations indicates: whethera respective point is to be included at the predicted point location,not included at the predicted point location, or relocated relative tothe predicted point location in a decompressed version of the pointcloud.
 16. A device, comprising: a memory storing program instructionsconfigured to implement a point cloud spatial decompression algorithm;and one or more processors, wherein the program instructions, whenexecuted on the one or more processors, cause the one or more processorsto: receive update information for predicted point locations betweenrespective points of a reduced-point version of a point cloud; for eachof respective ones of the points of the reduced-point version of thepoint cloud: predict a respective predicted point location between therespective point and a neighboring point in the reduced-point version ofthe point cloud; and apply the update information for the predictedpoint location; and generate a decompressed version of the point cloud,wherein the decompressed version of the point cloud comprises one ormore points at the predicted point locations to which the updateinformation has been applied.
 17. The device of claim 16, wherein theprogram instructions, further cause the device to: receive thereduced-point version of the point cloud, wherein the reduced-pointversion is a spatially compressed version of the point cloud thancomprises fewer points than an original version of the point cloud priorto being compressed.
 18. The device of claim 16, wherein to apply theupdate information for the predicted point locations, the programinstructions cause the one or more processors to: for each of respectiveones of the predicted point locations: determine whether to include, notinclude, or relocate a point at the respective predicted point locationin the decompressed version of the point cloud.
 19. The device of claim18, wherein to apply the update information for a given one of thepredicted point locations, the program instructions cause the one ormore processors to: determine a location for a point relocated relativeto the given one of the predicted point locations based on adding orsubtracting residual location information to one or more coordinates ofthe given one of the predicted point locations.
 20. The device of claim19, wherein to predict the respective predicted point locations, theprogram instructions cause the one or more processors, for each of therespective ones of the points of the reduced-point version of the pointcloud, to: identify a set of neighboring points, wherein the setcomprises a number of nearest neighbors to be used for predictionaccording to a value indicated in the received data for the compressedpoint cloud; and identify a location between the respective point andeach of the neighboring points of the set of neighboring points.